Computing complex visual features with retinal spike times.
Computing complex visual features with retinal spike times.
复制标题
DOI：
10.1371/journal.pone.0053063
复制
发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Meister M
中科院分区：
文献类型：
--
作者：
Gütig R;Gollisch T;Sompolinsky H;Meister M
感觉系统中的神经元不仅可以通过它们的放电速率来表示信息，还可以通过单个脉冲的精确时间来表示信息。例如，首先在蝾螈身上发现的某些视网膜神经节细胞，通过它们的第一次脉冲潜伏期来编码新图像的空间结构。在这里，我们探讨了这种时间代码如何被下游神经回路用于计算单个神经节细胞信号中无法获得的图像的复杂特征。为此，我们将实验观察到的来自视网膜神经节细胞群的尖峰序列输入到突触后整合的整合-激活模型中。该积分的突触权重根据最近引入的节奏学习规则进行调整。我们发现这个模型神经元可以在单个突触阶段完成复杂的视觉检测任务，而这需要多个阶段的神经元来代替神经尖峰计数。此外，该模型计算速度很快，每次传入仅使用单个尖峰，并且可以通过单个尖峰依次发出其决策信号。将这些分析扩展到模拟视网膜信号的大集合，我们表明该模型可以检测独立于其相位的视觉模式的方向，这一操作被认为是早期视觉处理的基本操作之一。我们分析了这些计算是如何工作的，并将该模型的性能与其他读取尖峰定时信息的方案进行了比较。这些结果表明，视网膜以一种有利于时域计算的方式将空间信息格式化为时间尖峰序列。此外，复杂的图像分析已经可以通过一个简单的集成和激活模型神经元来实现，这强调了快速神经计算具有峰值时间的能力和可行性。
Neurons in sensory systems can represent information not only by their firing rate, but also by the precise timing of individual spikes. For example, certain retinal ganglion cells, first identified in the salamander, encode the spatial structure of a new image by their first-spike latencies. Here we explore how this temporal code can be used by downstream neural circuits for computing complex features of the image that are not available from the signals of individual ganglion cells. To this end, we feed the experimentally observed spike trains from a population of retinal ganglion cells to an integrate-and-fire model of post-synaptic integration. The synaptic weights of this integration are tuned according to the recently introduced tempotron learning rule. We find that this model neuron can perform complex visual detection tasks in a single synaptic stage that would require multiple stages for neurons operating instead on neural spike counts. Furthermore, the model computes rapidly, using only a single spike per afferent, and can signal its decision in turn by just a single spike. Extending these analyses to large ensembles of simulated retinal signals, we show that the model can detect the orientation of a visual pattern independent of its phase, an operation thought to be one of the primitives in early visual processing. We analyze how these computations work and compare the performance of this model to other schemes for reading out spike-timing information. These results demonstrate that the retina formats spatial information into temporal spike sequences in a way that favors computation in the time domain. Moreover, complex image analysis can be achieved already by a simple integrate-and-fire model neuron, emphasizing the power and plausibility of rapid neural computing with spike times.
登录
查看更多内容
DOI：
10.1126/science.1194908
发表时间：
2010-11-05
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Freiwald WA;Tsao DY
通讯作者：
Tsao DY
影响因子：
5.5
作者：
DEMONASTERIO, FM;GOURAS, P
通讯作者：
GOURAS, P
影响因子：
1.9
作者：
Gollisch, Tim;Meister, Markus
通讯作者：
Meister, Markus
影响因子：
5.3
作者：
Boudreau, CE;Ferster, D
通讯作者：
Ferster, D
影响因子：
1.4
作者：
BRAINARD, DH
通讯作者：
BRAINARD, DH